Comments (6)
TF-IDF was rewritten/simplified, but not merged to current master yet. After i get that to master, i'll add example. However it's pretty straightforward:
- You feed your whole corpus into TF-IDF vectorizer, to build vocab with counts.
- Then you pass sentence/text you want to be vectorized using transform() method, and get INDArray that will contain vector for your sentence/text.
However, you should note that dense tf-idf vector might be really big.
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Hi, it would be great, if you could make an example of this with Classification. I think of something like the ParagraphVectors Classifier Example, which is awesome! Thanks!
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Hi @raver119 ,
I tried implementing tfidfvectorizer the way you suggested. I had several files in my corpus. I fed them all to the TF-IDF vectorizer, but something is not working right. I am getting '0.00' for all values in the INDArray, after performing the transform function on the text that I want to be vectorized. Please find attached the screenshot of my output window.
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Hi, have you guyz, implemented TF-IDF example using dl4j framework. If you have please share us and example. I've implemented my own manual way of getting tf-idf, its nice but takes way more longer time in big corpus. here's my code link [https://github.com/jageshmaharjan/TFIDF_Example] Also, i've used pyspark version which is fairly good.
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If there is still interest for this example we could transform this test into an example.
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Closing on lack of activity. Feel free to re-open or re-submit if working on the sample.
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